Josh Dillon, Last Revised January 2022
This notebook examines an individual antenna's performance over a whole season. This notebook parses information from each nightly rtp_summarynotebook (as saved to .csvs) and builds a table describing antenna performance. It also reproduces per-antenna plots from each auto_metrics notebook pertinent to the specific antenna.
import os
from IPython.display import display, HTML
display(HTML("<style>.container { width:100% !important; }</style>"))
# If you want to run this notebook locally, copy the output of the next cell into the next line of this cell.
# antenna = "004"
# csv_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/_rtp_summary_'
# auto_metrics_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/auto_metrics_inspect'
# os.environ["ANTENNA"] = antenna
# os.environ["CSV_FOLDER"] = csv_folder
# os.environ["AUTO_METRICS_FOLDER"] = auto_metrics_folder
# Use environment variables to figure out path to the csvs and auto_metrics
antenna = str(int(os.environ["ANTENNA"]))
csv_folder = os.environ["CSV_FOLDER"]
auto_metrics_folder = os.environ["AUTO_METRICS_FOLDER"]
print(f'antenna = "{antenna}"')
print(f'csv_folder = "{csv_folder}"')
print(f'auto_metrics_folder = "{auto_metrics_folder}"')
antenna = "221" csv_folder = "/home/obs/src/H6C_Notebooks/_rtp_summary_" auto_metrics_folder = "/home/obs/src/H6C_Notebooks/auto_metrics_inspect"
display(HTML(f'<h1 style=font-size:50px><u>Antenna {antenna} Report</u><p></p></h1>'))
import numpy as np
import pandas as pd
pd.set_option('display.max_rows', 1000)
import glob
import re
from hera_notebook_templates.utils import status_colors, Antenna
# load csvs and auto_metrics htmls in reverse chronological order
csvs = sorted(glob.glob(os.path.join(csv_folder, 'rtp_summary_table*.csv')))[::-1]
print(f'Found {len(csvs)} csvs in {csv_folder}')
auto_metric_htmls = sorted(glob.glob(auto_metrics_folder + '/auto_metrics_inspect_*.html'))[::-1]
print(f'Found {len(auto_metric_htmls)} auto_metrics notebooks in {auto_metrics_folder}')
Found 33 csvs in /home/obs/src/H6C_Notebooks/_rtp_summary_ Found 33 auto_metrics notebooks in /home/obs/src/H6C_Notebooks/auto_metrics_inspect
# Per-season options
mean_round_modz_cut = 4
dead_cut = 0.4
crossed_cut = 0.0
def jd_to_summary_url(jd):
return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H6C_Notebooks/blob/main/_rtp_summary_/rtp_summary_{jd}.html'
def jd_to_auto_metrics_url(jd):
return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H6C_Notebooks/blob/main/auto_metrics_inspect/auto_metrics_inspect_{jd}.html'
this_antenna = None
jds = []
# parse information about antennas and nodes
for csv in csvs:
df = pd.read_csv(csv)
for n in range(len(df)):
# Add this day to the antenna
row = df.loc[n]
if isinstance(row['Ant'], str) and '<a href' in row['Ant']:
antnum = int(row['Ant'].split('</a>')[0].split('>')[-1]) # it's a link, extract antnum
else:
antnum = int(row['Ant'])
if antnum != int(antenna):
continue
if np.issubdtype(type(row['Node']), np.integer):
row['Node'] = str(row['Node'])
if type(row['Node']) == str and row['Node'].isnumeric():
row['Node'] = 'N' + ('0' if len(row['Node']) == 1 else '') + row['Node']
if this_antenna is None:
this_antenna = Antenna(row['Ant'], row['Node'])
jd = [int(s) for s in re.split('_|\.', csv) if s.isdigit()][-1]
jds.append(jd)
this_antenna.add_day(jd, row)
break
# build dataframe
to_show = {'JDs': [f'<a href="{jd_to_summary_url(jd)}" target="_blank">{jd}</a>' for jd in jds]}
to_show['A Priori Status'] = [this_antenna.statuses[jd] for jd in jds]
df = pd.DataFrame(to_show)
# create bar chart columns for flagging percentages:
bar_cols = {}
bar_cols['Auto Metrics Flags'] = [this_antenna.auto_flags[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jee)'] = [this_antenna.dead_flags_Jee[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jnn)'] = [this_antenna.dead_flags_Jnn[jd] for jd in jds]
bar_cols['Crossed Fraction in Ant Metrics'] = [this_antenna.crossed_flags[jd] for jd in jds]
bar_cols['Flag Fraction Before Redcal'] = [this_antenna.flags_before_redcal[jd] for jd in jds]
bar_cols['Flagged By Redcal chi^2 Fraction'] = [this_antenna.redcal_flags[jd] for jd in jds]
for col in bar_cols:
df[col] = bar_cols[col]
z_score_cols = {}
z_score_cols['ee Shape Modified Z-Score'] = [this_antenna.ee_shape_zs[jd] for jd in jds]
z_score_cols['nn Shape Modified Z-Score'] = [this_antenna.nn_shape_zs[jd] for jd in jds]
z_score_cols['ee Power Modified Z-Score'] = [this_antenna.ee_power_zs[jd] for jd in jds]
z_score_cols['nn Power Modified Z-Score'] = [this_antenna.nn_power_zs[jd] for jd in jds]
z_score_cols['ee Temporal Variability Modified Z-Score'] = [this_antenna.ee_temp_var_zs[jd] for jd in jds]
z_score_cols['nn Temporal Variability Modified Z-Score'] = [this_antenna.nn_temp_var_zs[jd] for jd in jds]
z_score_cols['ee Temporal Discontinuties Modified Z-Score'] = [this_antenna.ee_temp_discon_zs[jd] for jd in jds]
z_score_cols['nn Temporal Discontinuties Modified Z-Score'] = [this_antenna.nn_temp_discon_zs[jd] for jd in jds]
for col in z_score_cols:
df[col] = z_score_cols[col]
ant_metrics_cols = {}
ant_metrics_cols['Average Dead Ant Metric (Jee)'] = [this_antenna.Jee_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Dead Ant Metric (Jnn)'] = [this_antenna.Jnn_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Crossed Ant Metric'] = [this_antenna.crossed_metrics[jd] for jd in jds]
for col in ant_metrics_cols:
df[col] = ant_metrics_cols[col]
redcal_cols = {}
redcal_cols['Median chi^2 Per Antenna (Jee)'] = [this_antenna.Jee_chisqs[jd] for jd in jds]
redcal_cols['Median chi^2 Per Antenna (Jnn)'] = [this_antenna.Jnn_chisqs[jd] for jd in jds]
for col in redcal_cols:
df[col] = redcal_cols[col]
# style dataframe
table = df.style.hide_index()\
.applymap(lambda val: f'background-color: {status_colors[val]}' if val in status_colors else '', subset=['A Priori Status']) \
.background_gradient(cmap='viridis', vmax=mean_round_modz_cut * 3, vmin=0, axis=None, subset=list(z_score_cols.keys())) \
.background_gradient(cmap='bwr_r', vmin=dead_cut-.25, vmax=dead_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
.background_gradient(cmap='bwr_r', vmin=crossed_cut-.25, vmax=crossed_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
.background_gradient(cmap='plasma', vmax=4, vmin=1, axis=None, subset=list(redcal_cols.keys())) \
.applymap(lambda val: 'font-weight: bold' if val < dead_cut else '', subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
.applymap(lambda val: 'font-weight: bold' if val < crossed_cut else '', subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
.applymap(lambda val: 'font-weight: bold' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
.applymap(lambda val: 'color: red' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
.bar(subset=list(bar_cols.keys()), vmin=0, vmax=1) \
.format({col: '{:,.4f}'.format for col in z_score_cols}) \
.format({col: '{:,.4f}'.format for col in ant_metrics_cols}) \
.format('{:,.2%}', na_rep='-', subset=list(bar_cols.keys())) \
.set_table_styles([dict(selector="th",props=[('max-width', f'70pt')])])
This table reproduces each night's row for this antenna from the RTP Summary notebooks. For more info on the columns, see those notebooks, linked in the JD column.
display(HTML(f'<h2>Antenna {antenna}, Node {this_antenna.node}:</h2>'))
HTML(table.render(render_links=True, escape=False))
| JDs | A Priori Status | Auto Metrics Flags | Dead Fraction in Ant Metrics (Jee) | Dead Fraction in Ant Metrics (Jnn) | Crossed Fraction in Ant Metrics | Flag Fraction Before Redcal | Flagged By Redcal chi^2 Fraction | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | Average Dead Ant Metric (Jee) | Average Dead Ant Metric (Jnn) | Average Crossed Ant Metric | Median chi^2 Per Antenna (Jee) | Median chi^2 Per Antenna (Jnn) |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 2460016 | RF_ok | 100.00% | 0.00% | 0.00% | 0.00% | - | - | -0.107418 | -0.349680 | -1.354866 | -0.759779 | 0.163836 | -1.229898 | 6.958813 | -0.177483 | 0.5531 | 0.5729 | 0.3469 | nan | nan |
| 2460015 | RF_ok | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 0.047244 | -0.423871 | -1.302016 | -0.807294 | -0.127166 | -1.114673 | 14.552461 | -0.823780 | 0.5664 | 0.5811 | 0.3442 | nan | nan |
| 2460014 | RF_ok | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 1.181198 | -0.202422 | -1.295728 | -0.828880 | 0.250001 | -0.876651 | 18.405453 | -0.351522 | 0.5332 | 0.5602 | 0.3486 | nan | nan |
| 2460013 | RF_ok | 100.00% | 0.00% | 0.00% | 0.00% | - | - | -0.097460 | -0.367207 | -1.256998 | -0.678881 | 0.300069 | -1.201674 | 22.148654 | -0.719730 | 0.5605 | 0.5823 | 0.3520 | nan | nan |
| 2460012 | RF_ok | 100.00% | 0.00% | 0.00% | 0.00% | - | - | -0.423646 | -0.446883 | -1.410294 | -0.875040 | 0.420031 | -1.176018 | 10.860658 | -0.933175 | 0.5631 | 0.5848 | 0.3452 | nan | nan |
| 2460011 | RF_ok | 100.00% | 0.00% | 0.00% | 0.00% | - | - | -0.229427 | -0.447682 | -0.684260 | -1.043160 | 12.343925 | -0.902694 | 6.739104 | -0.668883 | 0.5910 | 0.6063 | 0.3403 | nan | nan |
| 2460010 | RF_ok | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 0.321638 | -0.173798 | -0.852160 | -0.694948 | 0.519929 | -1.791116 | 6.660066 | -0.662869 | 0.5989 | 0.6234 | 0.3453 | nan | nan |
| 2460009 | RF_ok | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 0.452250 | -0.776635 | -0.831832 | -1.043060 | 3.476172 | -1.177241 | 16.102202 | -0.608951 | 0.6035 | 0.6255 | 0.3486 | nan | nan |
| 2460008 | RF_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | -0.090061 | -0.256969 | 0.000622 | -1.124679 | 1.891503 | -0.819953 | 0.619104 | -1.501129 | 0.6437 | 0.6604 | 0.3152 | nan | nan |
| 2460007 | RF_ok | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 1.458650 | -0.338061 | -1.366030 | -1.018919 | 0.943582 | -1.068974 | 9.919862 | -0.450742 | 0.5998 | 0.6293 | 0.3360 | nan | nan |
| 2459999 | RF_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | nan | nan | nan | nan | nan | nan | nan | nan | 0.5525 | 0.5970 | 0.3345 | nan | nan |
| 2459998 | RF_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | 0.924281 | -0.508856 | -0.904010 | -0.680822 | 0.465849 | -1.046983 | 3.056783 | -0.462108 | 0.5800 | 0.6110 | 0.3756 | nan | nan |
| 2459997 | RF_ok | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 0.885149 | -0.551991 | -0.683300 | -0.673349 | -0.396830 | -1.335370 | 4.840723 | -0.987933 | 0.5910 | 0.6217 | 0.3800 | nan | nan |
| 2459996 | RF_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | 0.550504 | -0.598990 | 0.014650 | -0.897333 | 2.795338 | -1.339112 | 0.736615 | -0.765957 | 0.6117 | 0.6303 | 0.3881 | nan | nan |
| 2459995 | RF_ok | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 1.420411 | -0.662048 | -0.590085 | -0.909270 | 8.423932 | -0.973242 | 5.440085 | -0.808088 | 0.5965 | 0.6252 | 0.3783 | nan | nan |
| 2459994 | RF_ok | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 2.439419 | -0.554449 | -1.177042 | -0.869833 | 1.352204 | -0.791803 | 8.822561 | -0.609752 | 0.5861 | 0.6188 | 0.3745 | nan | nan |
| 2459993 | RF_ok | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 2.781019 | -0.532240 | -0.804169 | -0.649476 | 2.782063 | -0.566860 | 6.192744 | -0.810453 | 0.5621 | 0.6096 | 0.3923 | nan | nan |
| 2459991 | RF_ok | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 3.263604 | -0.365384 | -0.904091 | -0.638841 | 4.746304 | -0.786064 | 6.579606 | -0.715323 | 0.5893 | 0.6129 | 0.3806 | nan | nan |
| 2459990 | RF_ok | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 3.289065 | -0.157002 | -1.217904 | -0.506576 | -0.007046 | -0.776762 | 15.489465 | -0.556724 | 0.5833 | 0.6143 | 0.3803 | nan | nan |
| 2459989 | RF_ok | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 3.363278 | -0.188756 | -0.928177 | -0.499345 | -0.386322 | -1.234788 | 12.531486 | -0.587717 | 0.5771 | 0.6150 | 0.3842 | nan | nan |
| 2459988 | RF_ok | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 4.049808 | -0.319994 | -1.299635 | -0.564183 | -0.308367 | -1.206783 | 12.967371 | -0.809425 | 0.5832 | 0.6183 | 0.3749 | nan | nan |
| 2459987 | RF_ok | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 0.747243 | -0.545981 | -0.601196 | -0.849628 | 7.905678 | -0.923788 | 4.926265 | -0.275366 | 0.5958 | 0.6206 | 0.3715 | nan | nan |
| 2459986 | RF_ok | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 1.804015 | -0.411704 | -0.965786 | -0.722463 | 5.199690 | -0.567108 | 2.299983 | -1.173762 | 0.6116 | 0.6431 | 0.3421 | nan | nan |
| 2459985 | RF_ok | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 1.411537 | -0.647110 | -0.730614 | -0.858111 | 6.635533 | -1.333078 | 11.175069 | -0.757285 | 0.5976 | 0.6224 | 0.3792 | nan | nan |
| 2459984 | RF_ok | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 0.770946 | -0.524640 | -0.076944 | -0.842040 | 5.795831 | -1.367137 | 1.329111 | -1.083911 | 0.6209 | 0.6406 | 0.3558 | nan | nan |
| 2459983 | RF_ok | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 0.324964 | -0.256988 | -0.793273 | -0.611509 | 10.083529 | -0.378835 | 1.575583 | -1.033804 | 0.6243 | 0.6569 | 0.3235 | nan | nan |
| 2459982 | RF_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | 1.269546 | -0.059730 | -1.123794 | -0.982258 | -0.322411 | -1.011368 | 0.256326 | -1.476034 | 0.6752 | 0.6847 | 0.2900 | nan | nan |
| 2459981 | RF_ok | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 1.326118 | 0.060050 | -1.202228 | -0.488358 | 0.998190 | -0.695671 | 10.611462 | -0.790135 | 0.5944 | 0.6233 | 0.3767 | nan | nan |
| 2459980 | RF_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | 0.613970 | -0.208519 | -1.340269 | -0.939529 | 0.554763 | -1.242295 | -0.415075 | -1.521831 | 0.6384 | 0.6578 | 0.3094 | nan | nan |
| 2459979 | RF_ok | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 1.140343 | 0.021048 | -1.308958 | -0.839548 | -0.446981 | -1.375444 | 5.131902 | -0.954037 | 0.5834 | 0.6173 | 0.3793 | nan | nan |
| 2459978 | RF_ok | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 1.167470 | 0.040878 | -1.259406 | -0.682438 | -0.296777 | -1.037731 | 10.597783 | -0.984611 | 0.5845 | 0.6159 | 0.3853 | nan | nan |
| 2459977 | RF_ok | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 0.705238 | -0.132179 | -1.246694 | -0.851534 | 0.489971 | -1.289203 | 6.431862 | -0.917289 | 0.5521 | 0.5812 | 0.3437 | nan | nan |
| 2459976 | RF_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | 0.990904 | 0.022600 | -1.379155 | -0.777319 | 0.152761 | -0.921903 | 2.310592 | -0.911550 | 0.5925 | 0.6229 | 0.3760 | nan | nan |
auto_metrics notebooks.¶htmls_to_display = []
for am_html in auto_metric_htmls:
html_to_display = ''
# read html into a list of lines
with open(am_html) as f:
lines = f.readlines()
# find section with this antenna's metric plots and add to html_to_display
jd = [int(s) for s in re.split('_|\.', am_html) if s.isdigit()][-1]
try:
section_start_line = lines.index(f'<h2>Antenna {antenna}: {jd}</h2>\n')
except ValueError:
continue
html_to_display += lines[section_start_line].replace(str(jd), f'<a href="{jd_to_auto_metrics_url(jd)}" target="_blank">{jd}</a>')
for line in lines[section_start_line + 1:]:
html_to_display += line
if '<hr' in line:
htmls_to_display.append(html_to_display)
break
These figures are reproduced from auto_metrics notebooks. For more info on the specific plots and metrics, see those notebooks (linked at the JD). The most recent 100 days (at most) are shown.
for i, html_to_display in enumerate(htmls_to_display):
if i == 100:
break
display(HTML(html_to_display))
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 221 | N18 | RF_ok | ee Temporal Discontinuties | 6.958813 | -0.349680 | -0.107418 | -0.759779 | -1.354866 | -1.229898 | 0.163836 | -0.177483 | 6.958813 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 221 | N18 | RF_ok | ee Temporal Discontinuties | 14.552461 | -0.423871 | 0.047244 | -0.807294 | -1.302016 | -1.114673 | -0.127166 | -0.823780 | 14.552461 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 221 | N18 | RF_ok | ee Temporal Discontinuties | 18.405453 | 1.181198 | -0.202422 | -1.295728 | -0.828880 | 0.250001 | -0.876651 | 18.405453 | -0.351522 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 221 | N18 | RF_ok | ee Temporal Discontinuties | 22.148654 | -0.097460 | -0.367207 | -1.256998 | -0.678881 | 0.300069 | -1.201674 | 22.148654 | -0.719730 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 221 | N18 | RF_ok | ee Temporal Discontinuties | 10.860658 | -0.423646 | -0.446883 | -1.410294 | -0.875040 | 0.420031 | -1.176018 | 10.860658 | -0.933175 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 221 | N18 | RF_ok | ee Temporal Variability | 12.343925 | -0.229427 | -0.447682 | -0.684260 | -1.043160 | 12.343925 | -0.902694 | 6.739104 | -0.668883 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 221 | N18 | RF_ok | ee Temporal Discontinuties | 6.660066 | 0.321638 | -0.173798 | -0.852160 | -0.694948 | 0.519929 | -1.791116 | 6.660066 | -0.662869 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 221 | N18 | RF_ok | ee Temporal Discontinuties | 16.102202 | 0.452250 | -0.776635 | -0.831832 | -1.043060 | 3.476172 | -1.177241 | 16.102202 | -0.608951 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 221 | N18 | RF_ok | ee Temporal Variability | 1.891503 | -0.256969 | -0.090061 | -1.124679 | 0.000622 | -0.819953 | 1.891503 | -1.501129 | 0.619104 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 221 | N18 | RF_ok | ee Temporal Discontinuties | 9.919862 | 1.458650 | -0.338061 | -1.366030 | -1.018919 | 0.943582 | -1.068974 | 9.919862 | -0.450742 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 221 | N18 | RF_ok | nn Shape | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 221 | N18 | RF_ok | ee Temporal Discontinuties | 3.056783 | 0.924281 | -0.508856 | -0.904010 | -0.680822 | 0.465849 | -1.046983 | 3.056783 | -0.462108 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 221 | N18 | RF_ok | ee Temporal Discontinuties | 4.840723 | 0.885149 | -0.551991 | -0.683300 | -0.673349 | -0.396830 | -1.335370 | 4.840723 | -0.987933 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 221 | N18 | RF_ok | ee Temporal Variability | 2.795338 | 0.550504 | -0.598990 | 0.014650 | -0.897333 | 2.795338 | -1.339112 | 0.736615 | -0.765957 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 221 | N18 | RF_ok | ee Temporal Variability | 8.423932 | 1.420411 | -0.662048 | -0.590085 | -0.909270 | 8.423932 | -0.973242 | 5.440085 | -0.808088 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 221 | N18 | RF_ok | ee Temporal Discontinuties | 8.822561 | 2.439419 | -0.554449 | -1.177042 | -0.869833 | 1.352204 | -0.791803 | 8.822561 | -0.609752 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 221 | N18 | RF_ok | ee Temporal Discontinuties | 6.192744 | 2.781019 | -0.532240 | -0.804169 | -0.649476 | 2.782063 | -0.566860 | 6.192744 | -0.810453 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 221 | N18 | RF_ok | ee Temporal Discontinuties | 6.579606 | 3.263604 | -0.365384 | -0.904091 | -0.638841 | 4.746304 | -0.786064 | 6.579606 | -0.715323 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 221 | N18 | RF_ok | ee Temporal Discontinuties | 15.489465 | -0.157002 | 3.289065 | -0.506576 | -1.217904 | -0.776762 | -0.007046 | -0.556724 | 15.489465 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 221 | N18 | RF_ok | ee Temporal Discontinuties | 12.531486 | -0.188756 | 3.363278 | -0.499345 | -0.928177 | -1.234788 | -0.386322 | -0.587717 | 12.531486 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 221 | N18 | RF_ok | ee Temporal Discontinuties | 12.967371 | -0.319994 | 4.049808 | -0.564183 | -1.299635 | -1.206783 | -0.308367 | -0.809425 | 12.967371 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 221 | N18 | RF_ok | ee Temporal Variability | 7.905678 | 0.747243 | -0.545981 | -0.601196 | -0.849628 | 7.905678 | -0.923788 | 4.926265 | -0.275366 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 221 | N18 | RF_ok | ee Temporal Variability | 5.199690 | -0.411704 | 1.804015 | -0.722463 | -0.965786 | -0.567108 | 5.199690 | -1.173762 | 2.299983 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 221 | N18 | RF_ok | ee Temporal Discontinuties | 11.175069 | -0.647110 | 1.411537 | -0.858111 | -0.730614 | -1.333078 | 6.635533 | -0.757285 | 11.175069 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 221 | N18 | RF_ok | ee Temporal Variability | 5.795831 | 0.770946 | -0.524640 | -0.076944 | -0.842040 | 5.795831 | -1.367137 | 1.329111 | -1.083911 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 221 | N18 | RF_ok | ee Temporal Variability | 10.083529 | 0.324964 | -0.256988 | -0.793273 | -0.611509 | 10.083529 | -0.378835 | 1.575583 | -1.033804 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 221 | N18 | RF_ok | ee Shape | 1.269546 | 1.269546 | -0.059730 | -1.123794 | -0.982258 | -0.322411 | -1.011368 | 0.256326 | -1.476034 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 221 | N18 | RF_ok | ee Temporal Discontinuties | 10.611462 | 0.060050 | 1.326118 | -0.488358 | -1.202228 | -0.695671 | 0.998190 | -0.790135 | 10.611462 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 221 | N18 | RF_ok | ee Shape | 0.613970 | -0.208519 | 0.613970 | -0.939529 | -1.340269 | -1.242295 | 0.554763 | -1.521831 | -0.415075 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 221 | N18 | RF_ok | ee Temporal Discontinuties | 5.131902 | 1.140343 | 0.021048 | -1.308958 | -0.839548 | -0.446981 | -1.375444 | 5.131902 | -0.954037 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 221 | N18 | RF_ok | ee Temporal Discontinuties | 10.597783 | 0.040878 | 1.167470 | -0.682438 | -1.259406 | -1.037731 | -0.296777 | -0.984611 | 10.597783 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 221 | N18 | RF_ok | ee Temporal Discontinuties | 6.431862 | 0.705238 | -0.132179 | -1.246694 | -0.851534 | 0.489971 | -1.289203 | 6.431862 | -0.917289 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 221 | N18 | RF_ok | ee Temporal Discontinuties | 2.310592 | 0.022600 | 0.990904 | -0.777319 | -1.379155 | -0.921903 | 0.152761 | -0.911550 | 2.310592 |